Association between Cybervictimization and Sleep Quality in a National Sample of U.S. Adolescents: Examining the Moderating Role of In-Person and Social Media Emotional Support

Association between Cybervictimization and Sleep Quality in a National Sample of U.S. Adolescents: Examining the Moderating Role of In-Person and Social Media Emotional Support | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between Cybervictimization and Sleep Quality in a National Sample of U.S. Adolescents: Examining the Moderating Role of In-Person and Social Media Emotional Support Christopher S. Chae, Giovanna Porta, Joshua Morgenlander, Candice L. Biernesser, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8502717/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Mar, 2026 Read the published version in BMC Pediatrics → Version 1 posted 12 You are reading this latest preprint version Abstract Background: Reduced sleep quality is common in adolescents, and youth from marginalized backgrounds are at heightened risk for cybervictimization, a known contributor to sleep problems. Emotional support has been linked to better sleep but its role in youth experiencing cybervictimization is unclear. We examined whether in-person and SM emotional support moderate the association between cybervictimization and sleep quality in a national sample of youth Methods: From November 2023 to March 2024, 689 youth of marginalized backgrounds (mean age 19, SD 1.52) recruited from social media completed a web-based survey assessing cybervictimization, sleep quality, in person peer emotional support, in person parent emotional support, and social media emotional support. Linear regression models tested moderation effects, adjusting for key demographic covariates. Results: Cybervictimization was associated with worse sleep quality (β = 3.034, 95% CI [1.766, 4.302], p < .001). All forms of emotional support were associated with better sleep quality: In-person peer (β = − 0.134, 95% CI [–0.195, − 0.072], p < .001), In-person parent (β = − 0.172, 95% CI [–0.228, − 0.115], p < .001), and SM-based (β = − 0.09, 95% CI [–0.169, − 0.01], p = .0271). Only in-person peer emotional support (β = 0.119, 95% CI [0.016, 0.222], p = .0236) significantly interacted with cybervictimization and sleep, showing a positive direction of effect. Conclusion: Cybervictimization was associated with worse sleep, while both in-person and SM emotional support were associated with better sleep. We also found a complex interaction where teens who had increased in-person peer emotional support, moderated worse sleep quality associated with cybervictimization. Cybervictimization Sleep Emotional Support Social Media Background Reduced sleep quality, (i.e., difficulty falling asleep, staying asleep, and feeling rested after waking up) in adolescents is a significant public health concern associated with widespread consequences for mental health, cognitive function, academic performance, and physical well-being [1—9]. More than half of middle school students and over two-thirds of high school students report short sleep duration less than the recommended 8 hours [10]. Emerging evidence indicates that adolescents from marginalized background, including those who identify as racial, sexual, or gender minorities and those who live in rural areas, experience significant sleep disparities characterized by shorter duration and poorer sleep quality compared with their peers [10-15]. To understand the drivers of sleep quality, increasing attention has turned toward adolescents’ digital lives. Adolescents are the most active internet users across age groups with 95% having access to a smartphone and half of U.S. teens reporting that they are online almost constantly [16, 17]. While many adolescents turn to social media to cultivate peer relationships [18], cybervictimization (i.e., intentional and repeated harm inflicted through electronic means) can be a co-occurring phenomenon of adolescents’ digital engagement leading to poor sleep quality [19-21]. Several studies have found that cybervictimization associates with shorter sleep duration, trouble falling, and trouble staying asleep independently from screen time [19, 20, 22]. While these findings highlight the general negative impact of cybervictimization on adolescent sleep, the link between cybervictimization and sleep quality in adolescents from marginalized backgrounds is yet to be established. This association is particularly important to examine, as recent studies indicate that racially and sexual and/or gender minority (SGM) adolescents face a disproportionately higher risk of online victimization [23-28]. Similarly, racial, SGM, as well as adolescents that live in rural areas have shorter sleep duration and decreased sleep quality than their peers [14, 15, 22, 29]. Additionally, given prior research linking peer victimization with decreased sleep quality in adolescents [30], it is possible that experiences of cybervictimization in this population will also be associated with reduced sleep quality. When considering protective factors of sleep quality in adolescents, perceived emotional support from family and peers have been linked to longer sleep duration, better sleep quality, and reduced nighttime awakenings in adolescents [31, 32]. This may be because supportive relationships may foster a sense of safety and reduce stress facilitating better sleep [33, 34]. Indeed, during stressful periods, the benefit of social support on increasing sleep quality is even greater [35]. Given these findings, it is possible that emotional support from both peer and parents will be directly associated with sleep quality because going to peers for emotional support is an important coping strategy [36, 37] especially for youth experiencing cybervictimization [38]. The possible protective role of SM emotional support has received comparatively less attention despite the importance of digital communication for these youth. For racial and SGM adolescents, social media can provide critical emotional and informational support that affirm identity and counteract victimization, even amid occasional harassment or exclusion [39-43]. Additionally, given previous evidence of social media usage helping adolescents with other health struggles loneliness, anxiety, depression, and stress [44-47], it is possible that SM emotional support could buffer the association between cybervictimization and sleep quality. Therefore, the goal of this study was twofold. First, we aimed to assess the association between cybervictimization and sleep quality in a national sample of adolescents from marginalized backgrounds, controlling for relevant sociodemographic characteristics. We hypothesized that cybervictimization will be associated with worse sleep quality. Second, we explored the moderating role of emotional support, both SM-based and in-person, in the association between cybervictimization and sleep quality. We hypothesized that all forms of support would moderate the effect of cybervictimization on sleep quality. Methods Recruitment methods and study population Recruitment took place from November 2023 to March 2024 through social media advertisements posted on Instagram, and TikTok, and Facebook, which directed interested viewers to the study website where they could learn more information and complete an eligibility screener. Recruitment from social media was appropriate given that social media use is ubiquitous in youth and Instagram and TikTok are among the most popular platforms among teens [48]. Given that we wanted to oversample adolescents from marginalized backgrounds, individuals were eligible to participate if they were between 13 and 21 years old, resided in the United States, and identified as at least one of the following groups: racial or ethnic minority, or sexual or gender minority, or living in rural areas. To identify fraudulent responses to the screener, we used a stringent set of criteria including completing the screener in under 30 seconds, completing the screener within 3 minutes of another response, accessing the screener outside of the study website, using an email address deemed fraudulent in one of our previous studies, and duplicate IP addresses, coordinates, and contact information. Individuals whose responses met any of our fraudulence criteria were not invited to participate in the survey as their response may be bot-generated or duplicates. Individuals whose responses were deemed legitimate and met study eligibility criteria were emailed a link to participate in the study. We received a total of 3965 responses to the screening. Out of these, 3276 (82.7%) were considered fraudulent per our criteria leaving 689 invited to participate the study. Eligible individuals provided informed consent before proceeding to the survey. Participants then completed a web-based survey developed by us specifically for this study. Surveys contained a battery of questions about mental health, social media experiences, psychosocial dimensions, and demographic characteristics. To protect the privacy of LGBTQ+ participants under 18 years of age from being accidentally outed to their parents/guardians, we obtained a waiver of parental consent. All recruitment and data collection procedures were approved by The University of Pittsburgh Institutional Review Board. Measures Sleep Quality Sleep quality was assessed using the PROMIS Pediatric Sleep Disturbance Short Form 8a (Cronbach’s alpha = .92) [49,50]. Items asked for participants’ agreement with statements such as “I had difficulty falling sleep” using a five-point scale to reflect the frequency of sleep problems over the past week: 1 = never, 2 = almost never, 3 = sometimes, 4 = almost always, and 5 = always. Higher T-scores reflect worse sleep quality. Cybervictimization Cybervictimization was assessed using a nine-item scale from the Cyber Peer Experiences Questionnaire (Cronbach’s alpha = .91) [51], which measures the frequency of online peer victimization. To elicit responses specifically relevant to the domain of interest, we added the specifier ‘on social media’ to each statement. Each item was rated on a five-point scale (0 = never, 1 = once or twice, 2 = a few times a month but not once a week, 3 = about once a week, and 4 = more than once a week) to statements such as “Someone posted mean things about you anonymously”. Higher scores indicate more frequent experiences of cybervictimization. Emotional Support In-person emotional support was measured with the parental and peer support subscales of the Child and Adolescent Social Support Scale (CASSS) [52]. Each subscale consists of 12 items rated on a six-point Likert scale (1 = never, 2 = almost never, 3 = some of the time, 4 = most of the time, 5 = almost always, and 6 = always) to answer statements such as “My parents show they are proud of me” for parent subscale and “My peers ask me to join activities” for peer subscale. Higher scores indicate more frequent perceived support. We used the CASSS subscales of parent (Cronbach’s alpha = .95) and peer emotional support (Cronbach’s alpha = .95) separately, consistent with prior confirmatory factor analyses demonstrating distinct factors and internal consistency for each source of support [53]. SM–based emotional support was measured using an adapted version of the PROMIS Emotional Support Short Form 4a (v2.0) (Cronbach’s alpha = .95) [54], modified to reflect social media contexts. Example items include “I have people on social media to listen to me when I need to talk” rated on a five-point scale: 1 = never, 2 = rarely, 3 = sometimes, 4 = usually, and 5 = always. Higher T-scores indicated greater perceived SM-based emotional support. Confirmatory Factor Analysis To evaluate whether the factor structure of the SM-adapted measures matched that of the original in-person version, we conducted a confirmatory factor analysis (CFA) using Mplus (version 8.10) [55]. The 1-factor solution for cybervictimization was acceptable: Comparative Fit Index (CFI)=0.950, Tucker-Lewis Index (TLI)=0.934, Root Mean Square Error of Approximation (RMSEA)=0.069, 90% confidence interval (CI)=0.082, Standardized Root Mean Square Residual (SRMR)=0.042. The 1-factor solution for SM-emotional support was similarly acceptable: CFI=0.996, TLI=0.989, RMSEA=0.086, 90% CI=0.135, SRMR=0.099. Statistical Analyses Distributions were visually assessed using histograms and boxplots. Of 689 participants, 676 (98.1%) had complete data on the variables of interest. We calculated standard descriptive statistics for all study variables. We conducted unadjusted bivariable analyses and an adjusted multivariable analysis to examine the associations between each predictor and the primary outcome. We then included in subsequent moderation models variables that demonstrated an association. We used simple linear regression to examine associations between cybervictimization (main predictor) and sleep quality (main outcome), as well as associations between in person and SM-based emotional support (our candidate moderating variables), and main predictor and outcome variables. We adjusted all models for age, sex at birth, gender identity, sexual orientation, race, ethnicity, socioeconomic status, and living arrangement. For categorical covariates with subgroups containing fewer than 20 participants, we combined those together to ensure sufficient power for statistical analyses. We estimated effect sizes using partial eta-squared. For categorical covariates with multiple levels, we performed Wald tests to evaluate their overall contribution. Statistical significance was defined as p < .05. However, variables were considered candidate moderators at p= 0.10 to reduce the risk of prematurely excluding potentially meaningful effects. All analyses were conducted using R (version 4.4.2) [56]. Results Univariable and bivariable analyses Characteristics of the study sample are displayed in Table 1. Participants had a mean age of 19 years (SD = 1.5). They were mostly assigned female at birth (76.8%) but diverse in gender identity (46.9% female, 24.2% male, 14.8% non-binary or transgender). Bisexual orientation was most common (26.7%), followed by heterosexual (22.9%), lesbian (14.4%), queer (13.8%), pansexual (7.3%), with remaining identities comprising the rest. The racial/ethnic makeup included 61.4% White, 12.5% Black, 12.5% multiracial, 11.6% Asian, and 1.5% American Indian/Alaskan Native and 0.1% Native Hawaiian/Other Pacific Islander; 19.3% identified as Latinx/Hispanic, and11.6% lived in a rural area. The mean cybervictimization score was 1.54 (SD = 0.63; sample range = 1–4.89), where higher scores indicate higher victimization (1 = lowest, 5 = highest). The average sleep quality T-score was 59.68 (SD = 9.95; sample range = 36.6–82.7), where higher t-scores reflect worse sleep quality (36.6 = best sleep quality, 82.7 = worst sleep quality). [Table 1 – all tables were uploaded as separate file each] As shown in Table 2, higher levels of cybervictimization were significantly associated with decreased sleep quality (β = 2.74, 95% CI [1.58, 3.90], p < .001). Conversely, all emotional support variables were associated with increased sleep quality. Greater peer emotional support (β = − 0.17, 95% CI [–0.23, − 0.11], p < .001), parent emotional support (β = − 0.22, 95% CI [–0.27, − 0.17], p < .001), and SM-based emotional support (β = − 0.07, 95% CI [–0.15, 0.01], p = .098) were each independently associated with increased sleep quality outcomes. [Table 2 – all tables were uploaded as separate file each] After adjusting for all covariates, each main predictor remained statistically significant and in the same direction as observed in unadjusted models (Table 3). Cybervictimization was associated with decreased sleep quality(β = 3.03, 95% CI [1.77, 4.30], p < .001), while all emotional support variables were associated with increased sleep quality: In-Person Peer Emotional Support (β = − 0.13, 95% CI [–0.20, − 0.07], p < .001), In-Person Parent Emotional Support (β = − 0.17, 95% CI [–0.23, − 0.12], p < .001), and SM-based Emotional Support (β = − 0.09, 95% CI [–0.17, − 0.01], p = .0244). [Table 3 – all tables were uploaded as separate file each] [Table 4 – all tables were uploaded as separate file each] Moderation Analyses As shown in Table 4, only in-person peer emotional support (β = 0.119, 95% CI [0.016, 0.222], p = 0.0236) significantly interacted with cybervictimization and sleep, showing a positive direction of effect indicating decreased sleep quality with more in person peer emotional support. In person parent emotional support (β = 0.047, 95% CI [-0.056, 0.149], p = .3706) and social medial based emotional support (β = 0.028, 95% CI [-0.148, 0.203], p = .7564) were not significant moderators of the increased cybervictimization to decreased sleep quality association. The in-person moderators remained significantly associated with increased sleep quality outcomes in the moderation analyses using interaction terms: In-person peer emotional support (β = -0.291, 95% CI [-0.456, -0.129], p < .001) and In-person parent emotional support (β = -0.233, 95% CI [-0.393, -0.074], p = 0.0041). Discussion This study is the first to examine the relationship between cybervictimization and sleep quality in adolescents from marginalized backgrounds. We also tested whether emotional support from peers and parents in person, and SM-based emotional support, moderated this relationship. In our national sample of 689 adolescents, cybervictimization was associated with worse sleep quality. In-person peer support, in-person parent support, and SM-based emotional support were each associated with better sleep quality after controlling for covariates. However, none of these forms of emotional support buffered the negative effects of cybervictimization on sleep. In fact, the only significant moderator, in-person peer emotional support, showed the opposite pattern: higher peer support was linked to a stronger negative association between cybervictimization and sleep quality. We found a significant association increased cybervictimization and decreased sleep quality, suggesting that cybervictimization may contribute to worse sleep in youth from marginalized backgrounds. This finding expands upon previous research that identified cybervictimization as a significant risk factor for reduced sleep quality in adolescents [ 19 – 21 ] and in-person victimization as a risk factor for decreased sleep quality in at-risk teens [ 30 ]. This suggests that cybervictimization might be a risk factor of worse sleep quality in marginalized adolescents, who are disproportionately affected by experiencing both higher rates of cybervictimization [ 23 – 26 ] and worse sleep quality [ 14 , 15 , 22 , 29 ]. We also found that higher levels of emotional support are associated with increase in sleep quality in adolescents from marginalized backgrounds in our sample. Prior research has shown that parental emotional support is strongly linked to increased sleep quality in adolescents [ 31 ], with subsequent work indicating that while family support showed the strongest support increased sleep quality, peer support also contributed to increased sleep quality [ 32 ]. Our findings extend the literature by demonstrating that in-person emotional support from peers and parents both are significantly associated with increased sleep quality among at-risk adolescents. Additionally, the existing literature has often highlighted the negative effects of social media use on adolescent sleep [ 57 , 58 ], particularly for adolescents [ 59 ]. However, our study aligns with others [ 60 , 61 ] in challenging the view that social media use is inherently harmful and instead argues that the quality and nature of the online interactions can make it beneficial or harmful. Consistent with this perspective, our results show that increased social media–based emotional support is significantly associated with increased sleep quality, suggesting that when adolescents engage in supportive and meaningful exchanges with peers online, it can be beneficial for their sleep. Yet our most interesting finding emerges when examining the potential moderators between the relationship of increased cybervictimization and decreased sleep quality. In this analysis, only in-person peer emotional support emerged as a significant moderator. This suggests that in-person emotional support may have a stronger influence on adolescents' sleep than online emotional support, despite the online nature of cybervictimization. However, contrary to our hypothesis, in-person peer social support showed a positive association, strengthening the association between cybervictimization and sleep quality. While one might expect online emotional relationships to mitigate the effects of cybervictimization, our results do not support this hypothesis. Additionally, the positive association between in-person emotional support and the impact of cybervictimization on sleep cannot be explained by the idea that more socially connected adolescents are likely to spend more time together thereby experiencing decreased sleep quality. This is because in bivariable analyses, higher levels of support were associated with better sleep quality. An alternative explanation may be that adolescents with stronger in-person peer social support are more sensitive to the effects of cybervictimization, whereas those with substantial SM-based emotional support appear less affected. Finally, it may be that adolescents with higher in-person peer support also experience greater in-person bullying, as victimization frequently spans both online and offline settings [ 62 , 63 ]. The overlap of these experiences might contribute to worse sleep quality. This unique moderation is particularly noteworthy because our study finds the presence of positive, meaningful emotional support may paradoxically heighten vulnerability to peer victimization. This nuance presents an interesting avenue for further exploration in future studies. This study is subject to several limitations. First, the cross-sectional nature of the design precludes the ability to draw conclusions about causality or to make predictions about the long-term effects or persistence of the observed associations. Second, the reliance on self-report introduces several potential sources of bias, including recall, social desirability, and misinterpretation of survey items. These biases may ultimately affect the validity and reliability of the data, limiting the generalizability and precision of the conclusions drawn from the study. Conclusion In this national sample of adolescents from marginalized backgrounds, higher cybervictimization was significantly strongly associated with decreased sleep quality. Conversely, greater in-person peer emotional support, in person parent emotional support, and social media based emotional support were all significantly associated with increased sleep quality, highlighting the similar protective effect of both in person and social media based emotional support. Only in person peer emotional support moderated the relationship between cybervictimization and sleep, and this was unexpectedly in the positive direction showing that greater in person peer emotional support corresponded with a worse sleep quality from cybervictimization. Together, these results suggest that emotional support may not consistently function as a protective factor in the relationship between cybervictimization and sleep, in fact the unexpected finding that higher in-person peer emotional support corresponded with poorer sleep among adolescents facing cybervictimization suggests that this interaction is more complex factors at play. Accordingly, recommendations that adolescents experiencing cybervictimization to seek emotional support in person or through social media should be made with the understanding that its protective effects may be more limited than expected. Abbreviations Social Media (SM), Sexual and/or Gender Minority (SGM) Declarations Ethics approval and consent to participate The study recruitment approach and procedures were approved by The University of Pittsburgh Institutional Review Board. Clinical trial number: not applicable. Consent for publication Not applicable. Availability of data and materials The datasets analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was supported by the Pitt Momentum Funds – Scaling Grants at the University of Pittsburgh. The funder had no role in the study design; data collection, analysis, or interpretation; or in the preparation of the manuscript. Authors’ contributions CSC developed the study, conducted the data analysis, and drafted the manuscript. CGE-V served as the principal investigator and provided overall study supervision, conceptual guidance, and critical revisions to the manuscript. GP provided statistical consultation and guidance on analytic strategy. JM contributed to drafting the introduction and managing the reference list. CLB and TRG contributed to manuscript review and provided substantive editorial feedback. 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Hopelab & Well Being Trust; 2018. https://wellbeingtrust.org/wp-content/uploads/2021/11/a-national-survey-by-hopelab-and-well-being-trust-2018.pdf. Accessed Nov 5, 2025. Marciano L, Ostroumova M, Schulz PJ, Camerini AL. Digital media use and adolescents’ mental health during the COVID-19 pandemic: a systematic review and meta-analysis. Front Public Health. 2022;9:793868. doi:10.3389/fpubh.2021.793868 Frison E, Eggermont S. The impact of daily stress on adolescents’ depressed mood: the role of social support seeking through Facebook. Comput Hum Behav. 2015;44:315–325. doi:10.1016/j.chb.2014.11.070 Cauberghe V, Van Wesenbeeck I, De Jans S, Hudders L, Ponnet K. How adolescents use social media to cope with feelings of loneliness and anxiety during COVID-19 lockdown. Cyberpsychol Behav Soc Netw. 2021;24(4):250–257. doi:10.1089/cyber.2020.0478 Nagata JM, Huang O, Hur JO, et al. Health benefits of social media use in adolescents and young adults. Curr Pediatr Rep. 2024;13(1):22. doi:10.1007/s40124-025-00357-7 Faverio M, Sidoti O. Teens, social media and AI chatbots 2025. Washington (DC): Pew Research Center; 2025 Dec 9 [cited 2025 Dec 30]. Available from: https://pewrsr.ch/3KOIf7i Bevans KB, Meltzer LJ, De La Motte A, Kratchman A, Viél D, Forrest CB. Qualitative development and content validation of the PROMIS pediatric sleep health items. Behav Sleep Med. 2018;16(3):1–15. doi:10.1080/15402002.2016.1266492 Forrest CB, Meltzer LJ, Marcus CL, de la Motte A, Kratchman A, Buysse DJ, et al. Development and validation of the PROMIS pediatric sleep disturbance and sleep-related impairment item banks. Sleep. 2018;41(6). doi:10.1093/sleep/zsy051 Landoll RR, La Greca AM, Lai BS, Chan SF, Herge WM. Cyber victimization by peers: prospective associations with adolescent social anxiety and depressive symptoms. J Adolesc. 2015;42:77–86. doi:10.1016/j.adolescence.2015.04.002 Malecki CK, Demaray MK, Elliott SN, Nolten PW. Child and Adolescent Social Support Scale (CASSS). APA PsycTests. doi:10.1037/t57891-000 Demaray MK, Malecki CK. The relationship between perceived social support and maladjustment for students at risk. Psychol Sch. 2002;39(3):305–316. doi:10.1002/pits.10018 Hahn EA, DeWalt DA, Bode RK, Garcia SF, DeVellis RF, Correia H, et al. New English and Spanish social health measures will facilitate evaluating health determinants. Health Psychol. 2014;33(5):490–499. doi:10.1037/hea0000038 Muthén LK, Muthén BO. Mplus (version 8.10) [computer software]. Los Angeles (CA): Muthén & Muthén; 2024. R Core Team. R: a language and environment for statistical computing. Version 4.4.2. Vienna (Austria): R Foundation for Statistical Computing; 2024. https://www.R-project.org/. Yu DJ, Wing YK, Li TMH, Chan NY. The impact of social media use on sleep and mental health in youth: a scoping review. Curr Psychiatry Rep. 2024;26(3):104–119. doi:10.1007/s11920-024-01481-9 Alonzo R, Hussain J, Stranges S, Anderson KK. Interplay between social media use, sleep quality, and mental health in youth: a systematic review. Sleep Med Rev. 2021;56:101414. doi:10.1016/j.smrv.2020.101414 Volpe VV, Benson GP, Czoty L, et al. Not just time on social media: experiences of online racial/ethnic discrimination and worse sleep quality for Black, Latinx, Asian, and multiracial young adults. J Racial Ethn Health Disparities. 2023;10:2312–2319. doi:10.1007/s40615-022-01410-7 McAlister KL, Beatty CC, Smith Caswell JE, Yourell JL, Huberty JL. Social media use in adolescents: bans, benefits, and emotion regulation behaviors. JMIR Ment Health. 2024;11:e64626. doi:10.2196/64626 Popat A, Tarrant C. Exploring adolescents’ perspectives on social media and mental health and well-being: a qualitative literature review. Clin Child Psychol Psychiatry. 2022;28(1):323–337. doi:10.1177/13591045221092884 Kowalski RM, Giumetti GW, Schroeder AN, Lattanner MR. Bullying in the digital age: a critical review and meta-analysis of cyberbullying research among youth. Psychol Bull. 2014;140(4):1073–1137. doi:10.1037/a0035618 Waasdorp TE, Bradshaw CP. The overlap between cyberbullying and traditional bullying. J Adolesc Health. 2015;56(5):483–488. doi:10.1016/j.jadohealth.2014.12.002 Tables Tables 1 to 4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.xlsx Table3.xlsx Table2.xlsx Table4.xlsx SupplementaryMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 18 Mar, 2026 Read the published version in BMC Pediatrics → Version 1 posted Editorial decision: Revision requested 09 Feb, 2026 Reviews received at journal 08 Feb, 2026 Reviews received at journal 02 Feb, 2026 Reviewers agreed at journal 30 Jan, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers agreed at journal 28 Jan, 2026 Reviewers invited by journal 28 Jan, 2026 Editor invited by journal 19 Jan, 2026 Editor assigned by journal 07 Jan, 2026 Submission checks completed at journal 06 Jan, 2026 First submitted to journal 06 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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More than half of middle school students and over two-thirds of high school students report short sleep duration less than the recommended 8 hours [10]. Emerging evidence indicates that adolescents from marginalized background, including those who identify as racial, sexual, or gender minorities and those who live in rural areas, experience significant sleep disparities characterized by shorter duration and poorer sleep quality compared with their peers [10-15]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo understand the drivers of sleep quality, increasing attention has turned toward adolescents\u0026rsquo; digital lives. Adolescents are the most active internet users across age groups with 95% having access to a smartphone and half of U.S. teens reporting that they are online almost constantly [16, 17]. While many adolescents turn to social media to cultivate peer relationships [18], cybervictimization (i.e., intentional and repeated harm inflicted through electronic means) can be a co-occurring phenomenon of adolescents\u0026rsquo; digital engagement leading to poor sleep quality [19-21]. Several studies have found that cybervictimization associates with shorter sleep duration, trouble falling, and trouble staying asleep independently from screen time [19, 20, 22]. While these findings highlight the general negative impact of cybervictimization on adolescent sleep, the link between cybervictimization and sleep quality in adolescents from marginalized backgrounds is yet to be established. This association is particularly important to examine, as recent studies indicate that racially and sexual and/or gender minority (SGM) adolescents face a disproportionately higher risk of online victimization [23-28]. Similarly, racial, SGM, as well as adolescents that live in rural areas have shorter sleep duration and decreased sleep quality than their peers [14, 15, 22, 29]. Additionally, given prior research linking peer victimization with decreased sleep quality in adolescents [30], it is possible that experiences of cybervictimization in this population will also be associated with reduced sleep quality.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen considering protective factors of sleep quality in adolescents, perceived emotional support from family and peers have been linked to longer sleep duration, better sleep quality, and reduced nighttime awakenings in adolescents [31, 32]. This may be because supportive relationships may foster a sense of safety and reduce stress facilitating better sleep [33, 34]. Indeed, during stressful periods, the benefit of social support on increasing sleep quality is even greater [35]. Given these findings, it is possible that emotional support from both peer and parents will be directly associated with sleep quality because going to peers for emotional support is an important coping strategy [36, 37] especially for youth experiencing cybervictimization [38]. \u0026nbsp;The possible protective role of SM emotional support has received comparatively less attention despite the importance of digital communication for these youth. For racial and SGM adolescents, social media can provide critical emotional and informational support that affirm identity and counteract victimization, even amid occasional harassment or exclusion [39-43]. Additionally, given previous evidence of social media usage helping adolescents with other health struggles loneliness, anxiety, depression, and stress [44-47], it is possible that SM emotional support could buffer the association between cybervictimization and sleep quality. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTherefore, the goal of this study was twofold. First, we aimed to assess the association between cybervictimization and sleep quality in a national sample of adolescents from marginalized backgrounds, controlling for relevant sociodemographic characteristics. We hypothesized that cybervictimization will be associated with worse sleep quality. Second, we explored the moderating role of emotional support, both SM-based and in-person, in the association between cybervictimization and sleep quality. We hypothesized that all forms of support would moderate the effect of cybervictimization on sleep quality.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eRecruitment methods and study population\u003c/p\u003e\n\u003cp\u003eRecruitment took place from November 2023 to March 2024 through social media advertisements posted on Instagram, and TikTok, and Facebook, which directed interested viewers to the study website where they could learn more information and complete an eligibility screener. Recruitment from social media was appropriate given that social media use is ubiquitous in youth and Instagram and TikTok are among the most popular platforms among teens [48]. Given that we wanted to oversample adolescents from marginalized backgrounds, individuals were eligible to participate if they were between 13 and 21 years old, resided in the United States, and identified as at least one of the following groups: racial or ethnic minority, or sexual or gender minority, or living in rural areas. To identify fraudulent responses to the screener, we used a stringent set of criteria including completing the screener in under 30 seconds, completing the screener within 3 minutes of another response, accessing the screener outside of the study website, using an email address deemed fraudulent in one of our previous studies, and duplicate IP addresses, coordinates, and contact information. Individuals whose responses met any of our fraudulence criteria were not invited to participate in the survey as their response may be bot-generated or duplicates. Individuals whose responses were deemed legitimate and met study eligibility criteria were emailed a link to participate in the study. We received a total of 3965 responses to the screening. Out of these, 3276 (82.7%) were considered fraudulent per our criteria leaving 689 invited to participate the study. Eligible individuals provided informed consent before proceeding to the survey. Participants then completed a web-based survey developed by us specifically for this study. Surveys contained a battery of questions about mental health, social media experiences, psychosocial dimensions, and demographic characteristics. To protect the privacy of LGBTQ+ participants under 18 years of age from being accidentally outed to their parents/guardians, we obtained a waiver of parental consent. All recruitment and data collection procedures were approved by The University of Pittsburgh Institutional Review Board.\u003c/p\u003e\n\u003cp\u003eMeasures\u003c/p\u003e\n\u003cp\u003eSleep Quality\u003c/p\u003e\n\u003cp\u003eSleep quality was assessed using the PROMIS Pediatric Sleep Disturbance Short Form 8a (Cronbach\u0026rsquo;s alpha = .92) [49,50]. Items asked for participants\u0026rsquo; agreement with statements such as \u0026ldquo;I had difficulty falling sleep\u0026rdquo; using a five-point scale to reflect the frequency of sleep problems over the past week: 1 = never, 2 = almost never, 3 = sometimes, 4 = almost always, and 5 = always. Higher T-scores reflect worse sleep quality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCybervictimization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCybervictimization was assessed using a nine-item scale from the Cyber Peer Experiences Questionnaire (Cronbach\u0026rsquo;s alpha = .91) [51], which measures the frequency of online peer victimization. To elicit responses specifically relevant to the domain of interest, we added the specifier \u0026lsquo;on social media\u0026rsquo; to each statement. Each item was rated on a five-point scale (0 = never, 1 = once or twice, 2 = a few times a month but not once a week, 3 = about once a week, and 4 = more than once a week) to statements such as \u0026ldquo;Someone posted mean things about you anonymously\u0026rdquo;. Higher scores indicate more frequent experiences of cybervictimization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEmotional Support\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn-person emotional support was measured with the parental and peer support subscales of the Child and Adolescent Social Support Scale (CASSS) [52]. Each subscale consists of 12 items rated on a six-point Likert scale (1 = never, 2 = almost never, 3 = some of the time, 4 = most of the time, 5 = almost always, and 6 = always) to answer statements such as \u0026ldquo;My parents show they are proud of me\u0026rdquo; for parent subscale and \u0026ldquo;My peers ask me to join activities\u0026rdquo; for peer subscale. Higher scores indicate more frequent perceived support. We used the CASSS subscales of parent (Cronbach\u0026rsquo;s alpha = .95) and peer emotional support (Cronbach\u0026rsquo;s alpha = .95) separately, consistent with prior confirmatory factor analyses demonstrating distinct factors and internal consistency for each source of support [53].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSM\u0026ndash;based emotional support was measured using an adapted version of the PROMIS Emotional Support Short Form 4a (v2.0) (Cronbach\u0026rsquo;s alpha = .95) [54], modified to reflect social media contexts. Example items include \u0026ldquo;I have people on social media to listen to me when I need to talk\u0026rdquo; rated on a five-point scale: 1 = never, 2 = rarely, 3 = sometimes, 4 = usually, and 5 = always. Higher T-scores indicated greater perceived SM-based emotional support.\u003c/p\u003e\n\u003cp\u003eConfirmatory Factor Analysis\u003c/p\u003e\n\u003cp\u003eTo evaluate whether the factor structure of the SM-adapted measures matched that of the original in-person version, we conducted a confirmatory factor analysis (CFA) using Mplus (version 8.10) [55].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe 1-factor solution for cybervictimization was acceptable: Comparative Fit Index (CFI)=0.950, Tucker-Lewis Index (TLI)=0.934, Root Mean Square Error of Approximation (RMSEA)=0.069, 90% confidence interval (CI)=0.082, Standardized Root Mean Square Residual (SRMR)=0.042.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe 1-factor solution for SM-emotional support was similarly acceptable: CFI=0.996, TLI=0.989, RMSEA=0.086, 90% CI=0.135, SRMR=0.099.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical Analyses\u003c/p\u003e\n\u003cp\u003eDistributions were visually assessed using histograms and boxplots. Of 689 participants, 676 (98.1%) had complete data on the variables of interest. We calculated standard descriptive statistics for all study variables. We conducted unadjusted bivariable analyses and an adjusted multivariable analysis to examine the associations between each predictor and the primary outcome. We then included in subsequent moderation models variables that demonstrated an association. We used simple linear regression to examine associations between cybervictimization (main predictor) and sleep quality (main outcome), as well as associations between in person and SM-based emotional support (our candidate moderating variables), and main predictor and outcome variables.\u003c/p\u003e\n\u003cp\u003eWe adjusted all models for age, sex at birth, gender identity, sexual orientation, race, ethnicity, socioeconomic status, and living arrangement. For categorical covariates with subgroups containing fewer than 20 participants, we combined those together to ensure sufficient power for statistical analyses. We estimated effect sizes using partial eta-squared. For categorical covariates with multiple levels, we performed Wald tests to evaluate their overall contribution. Statistical significance was defined as p \u0026lt; .05. However, variables were considered candidate moderators at p= 0.10 to reduce the risk of prematurely excluding potentially meaningful effects.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll analyses were conducted using R (version 4.4.2) [56].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eUnivariable and bivariable analyses\u003c/p\u003e \u003cp\u003eCharacteristics of the study sample are displayed in Table\u0026nbsp;1. Participants had a mean age of 19 years (SD\u0026thinsp;=\u0026thinsp;1.5). They were mostly assigned female at birth (76.8%) but diverse in gender identity (46.9% female, 24.2% male, 14.8% non-binary or transgender). Bisexual orientation was most common (26.7%), followed by heterosexual (22.9%), lesbian (14.4%), queer (13.8%), pansexual (7.3%), with remaining identities comprising the rest. The racial/ethnic makeup included 61.4% White, 12.5% Black, 12.5% multiracial, 11.6% Asian, and 1.5% American Indian/Alaskan Native and 0.1% Native Hawaiian/Other Pacific Islander; 19.3% identified as Latinx/Hispanic, and11.6% lived in a rural area. The mean cybervictimization score was 1.54 (SD\u0026thinsp;=\u0026thinsp;0.63; sample range\u0026thinsp;=\u0026thinsp;1\u0026ndash;4.89), where higher scores indicate higher victimization (1\u0026thinsp;=\u0026thinsp;lowest, 5\u0026thinsp;=\u0026thinsp;highest). The average sleep quality T-score was 59.68 (SD\u0026thinsp;=\u0026thinsp;9.95; sample range\u0026thinsp;=\u0026thinsp;36.6\u0026ndash;82.7), where higher t-scores reflect worse sleep quality (36.6\u0026thinsp;=\u0026thinsp;best sleep quality, 82.7\u0026thinsp;=\u0026thinsp;worst sleep quality).\u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;1 \u0026ndash; all tables were uploaded as separate file each]\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;2, higher levels of cybervictimization were significantly associated with decreased sleep quality (β\u0026thinsp;=\u0026thinsp;2.74, 95% CI [1.58, 3.90], p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Conversely, all emotional support variables were associated with increased sleep quality. Greater peer emotional support (β = \u0026minus;\u0026thinsp;0.17, 95% CI [\u0026ndash;0.23, \u0026minus;\u0026thinsp;0.11], p\u0026thinsp;\u0026lt;\u0026thinsp;.001), parent emotional support (β = \u0026minus;\u0026thinsp;0.22, 95% CI [\u0026ndash;0.27, \u0026minus;\u0026thinsp;0.17], p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and SM-based emotional support (β = \u0026minus;\u0026thinsp;0.07, 95% CI [\u0026ndash;0.15, 0.01], p\u0026thinsp;=\u0026thinsp;.098) were each independently associated with increased sleep quality outcomes.\u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;2 \u0026ndash; all tables were uploaded as separate file each]\u003c/p\u003e \u003cp\u003eAfter adjusting for all covariates, each main predictor remained statistically significant and in the same direction as observed in unadjusted models (Table\u0026nbsp;3). Cybervictimization was associated with decreased sleep quality(β\u0026thinsp;=\u0026thinsp;3.03, 95% CI [1.77, 4.30], p\u0026thinsp;\u0026lt;\u0026thinsp;.001), while all emotional support variables were associated with increased sleep quality: In-Person Peer Emotional Support (β = \u0026minus;\u0026thinsp;0.13, 95% CI [\u0026ndash;0.20, \u0026minus;\u0026thinsp;0.07], p\u0026thinsp;\u0026lt;\u0026thinsp;.001), In-Person Parent Emotional Support (β = \u0026minus;\u0026thinsp;0.17, 95% CI [\u0026ndash;0.23, \u0026minus;\u0026thinsp;0.12], p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and SM-based Emotional Support (β = \u0026minus;\u0026thinsp;0.09, 95% CI [\u0026ndash;0.17, \u0026minus;\u0026thinsp;0.01], p\u0026thinsp;=\u0026thinsp;.0244).\u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;3 \u0026ndash; all tables were uploaded as separate file each]\u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;4 \u0026ndash; all tables were uploaded as separate file each]\u003c/p\u003e \u003cp\u003eModeration Analyses\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;4, only in-person peer emotional support (β\u0026thinsp;=\u0026thinsp;0.119, 95% CI [0.016, 0.222], p\u0026thinsp;=\u0026thinsp;0.0236) significantly interacted with cybervictimization and sleep, showing a positive direction of effect indicating decreased sleep quality with more in person peer emotional support. In person parent emotional support (β\u0026thinsp;=\u0026thinsp;0.047, 95% CI [-0.056, 0.149], p\u0026thinsp;=\u0026thinsp;.3706) and social medial based emotional support (β\u0026thinsp;=\u0026thinsp;0.028, 95% CI [-0.148, 0.203], p\u0026thinsp;=\u0026thinsp;.7564) were not significant moderators of the increased cybervictimization to decreased sleep quality association. The in-person moderators remained significantly associated with increased sleep quality outcomes in the moderation analyses using interaction terms: In-person peer emotional support (β = -0.291, 95% CI [-0.456, -0.129], p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and In-person parent emotional support (β = -0.233, 95% CI [-0.393, -0.074], p\u0026thinsp;=\u0026thinsp;0.0041).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is the first to examine the relationship between cybervictimization and sleep quality in adolescents from marginalized backgrounds. We also tested whether emotional support from peers and parents in person, and SM-based emotional support, moderated this relationship. In our national sample of 689 adolescents, cybervictimization was associated with worse sleep quality. In-person peer support, in-person parent support, and SM-based emotional support were each associated with better sleep quality after controlling for covariates. However, none of these forms of emotional support buffered the negative effects of cybervictimization on sleep. In fact, the only significant moderator, in-person peer emotional support, showed the opposite pattern: higher peer support was linked to a stronger negative association between cybervictimization and sleep quality.\u003c/p\u003e \u003cp\u003eWe found a significant association increased cybervictimization and decreased sleep quality, suggesting that cybervictimization may contribute to worse sleep in youth from marginalized backgrounds. This finding expands upon previous research that identified cybervictimization as a significant risk factor for reduced sleep quality in adolescents [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and in-person victimization as a risk factor for decreased sleep quality in at-risk teens [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This suggests that cybervictimization might be a risk factor of worse sleep quality in marginalized adolescents, who are disproportionately affected by experiencing both higher rates of cybervictimization [\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and worse sleep quality [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe also found that higher levels of emotional support are associated with increase in sleep quality in adolescents from marginalized backgrounds in our sample. Prior research has shown that parental emotional support is strongly linked to increased sleep quality in adolescents [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], with subsequent work indicating that while family support showed the strongest support increased sleep quality, peer support also contributed to increased sleep quality [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Our findings extend the literature by demonstrating that in-person emotional support from peers and parents both are significantly associated with increased sleep quality among at-risk adolescents.\u003c/p\u003e \u003cp\u003eAdditionally, the existing literature has often highlighted the negative effects of social media use on adolescent sleep [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], particularly for adolescents [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. However, our study aligns with others [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] in challenging the view that social media use is inherently harmful and instead argues that the quality and nature of the online interactions can make it beneficial or harmful. Consistent with this perspective, our results show that increased social media\u0026ndash;based emotional support is significantly associated with increased sleep quality, suggesting that when adolescents engage in supportive and meaningful exchanges with peers online, it can be beneficial for their sleep.\u003c/p\u003e \u003cp\u003eYet our most interesting finding emerges when examining the potential moderators between the relationship of increased cybervictimization and decreased sleep quality. In this analysis, only in-person peer emotional support emerged as a significant moderator. This suggests that in-person emotional support may have a stronger influence on adolescents' sleep than online emotional support, despite the online nature of cybervictimization. However, contrary to our hypothesis, in-person peer social support showed a positive association, strengthening the association between cybervictimization and sleep quality. While one might expect online emotional relationships to mitigate the effects of cybervictimization, our results do not support this hypothesis. Additionally, the positive association between in-person emotional support and the impact of cybervictimization on sleep cannot be explained by the idea that more socially connected adolescents are likely to spend more time together thereby experiencing decreased sleep quality. This is because in bivariable analyses, higher levels of support were associated with better sleep quality. An alternative explanation may be that adolescents with stronger in-person peer social support are more sensitive to the effects of cybervictimization, whereas those with substantial SM-based emotional support appear less affected. Finally, it may be that adolescents with higher in-person peer support also experience greater in-person bullying, as victimization frequently spans both online and offline settings [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. The overlap of these experiences might contribute to worse sleep quality. This unique moderation is particularly noteworthy because our study finds the presence of positive, meaningful emotional support may paradoxically heighten vulnerability to peer victimization. This nuance presents an interesting avenue for further exploration in future studies.\u003c/p\u003e \u003cp\u003eThis study is subject to several limitations. First, the cross-sectional nature of the design precludes the ability to draw conclusions about causality or to make predictions about the long-term effects or persistence of the observed associations. Second, the reliance on self-report introduces several potential sources of bias, including recall, social desirability, and misinterpretation of survey items. These biases may ultimately affect the validity and reliability of the data, limiting the generalizability and precision of the conclusions drawn from the study.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this national sample of adolescents from marginalized backgrounds, higher cybervictimization was significantly strongly associated with decreased sleep quality. Conversely, greater in-person peer emotional support, in person parent emotional support, and social media based emotional support were all significantly associated with increased sleep quality, highlighting the similar protective effect of both in person and social media based emotional support. Only in person peer emotional support moderated the relationship between cybervictimization and sleep, and this was unexpectedly in the positive direction showing that greater in person peer emotional support corresponded with a worse sleep quality from cybervictimization. Together, these results suggest that emotional support may not consistently function as a protective factor in the relationship between cybervictimization and sleep, in fact the unexpected finding that higher in-person peer emotional support corresponded with poorer sleep among adolescents facing cybervictimization suggests that this interaction is more complex factors at play. Accordingly, recommendations that adolescents experiencing cybervictimization to seek emotional support in person or through social media should be made with the understanding that its protective effects may be more limited than expected.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eSocial Media (SM), Sexual and/or Gender Minority (SGM)\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe study recruitment approach and procedures were approved by The University of Pittsburgh Institutional Review Board. Clinical trial number: not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Pitt Momentum Funds \u0026ndash; Scaling Grants at the University of Pittsburgh. The funder had no role in the study design; data collection, analysis, or interpretation; or in the preparation of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eCSC developed the study, conducted the data analysis, and drafted the manuscript. CGE-V served as the principal investigator and provided overall study supervision, conceptual guidance, and critical revisions to the manuscript. GP provided statistical consultation and guidance on analytic strategy. JM contributed to drafting the introduction and managing the reference list. CLB and TRG contributed to manuscript review and provided substantive editorial feedback. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors thank the Momentum study team for data collection and study coordination.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; information\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAstill RG, van der Heijden KB, van IJzendoorn MH, van Someren EJW. Sleep, cognition, and behavioral problems in school-age children: a century of research meta-analyzed. Psychol Bull. 2012;138(6):1109\u0026ndash;1138. doi:10.1037/a0028204\u003c/li\u003e\n\u003cli\u003eCooper R, Di Biase MA, Bei B, Quach J, Cropley V. Associations of changes in sleep and emotional and behavioral problems from late childhood to early adolescence. JAMA Psychiatry. 2023;80(6):585\u0026ndash;596. doi:10.1001/jamapsychiatry.2023.0379\u003c/li\u003e\n\u003cli\u003eFatima Y, Doi SAR, Mamun AA. 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Digital health practices, social media use, and mental well-being among teens and young adults in the U.S.: a national survey sponsored by Hopelab and Well Being Trust. Hopelab \u0026amp; Well Being Trust; 2018. https://wellbeingtrust.org/wp-content/uploads/2021/11/a-national-survey-by-hopelab-and-well-being-trust-2018.pdf. Accessed Nov 5, 2025.\u003c/li\u003e\n\u003cli\u003eMarciano L, Ostroumova M, Schulz PJ, Camerini AL. Digital media use and adolescents\u0026rsquo; mental health during the COVID-19 pandemic: a systematic review and meta-analysis. Front Public Health. 2022;9:793868. doi:10.3389/fpubh.2021.793868\u003c/li\u003e\n\u003cli\u003eFrison E, Eggermont S. The impact of daily stress on adolescents\u0026rsquo; depressed mood: the role of social support seeking through Facebook. Comput Hum Behav. 2015;44:315\u0026ndash;325. doi:10.1016/j.chb.2014.11.070\u003c/li\u003e\n\u003cli\u003eCauberghe V, Van Wesenbeeck I, De Jans S, Hudders L, Ponnet K. How adolescents use social media to cope with feelings of loneliness and anxiety during COVID-19 lockdown. Cyberpsychol Behav Soc Netw. 2021;24(4):250\u0026ndash;257. doi:10.1089/cyber.2020.0478\u003c/li\u003e\n\u003cli\u003eNagata JM, Huang O, Hur JO, et al. Health benefits of social media use in adolescents and young adults. Curr Pediatr Rep. 2024;13(1):22. doi:10.1007/s40124-025-00357-7\u003c/li\u003e\n\u003cli\u003eFaverio M, Sidoti O. Teens, social media and AI chatbots 2025. Washington (DC): Pew Research Center; 2025 Dec 9 [cited 2025 Dec 30]. Available from: https://pewrsr.ch/3KOIf7i\u003c/li\u003e\n\u003cli\u003eBevans KB, Meltzer LJ, De La Motte A, Kratchman A, Vi\u0026eacute;l D, Forrest CB. Qualitative development and content validation of the PROMIS pediatric sleep health items. Behav Sleep Med. 2018;16(3):1\u0026ndash;15. doi:10.1080/15402002.2016.1266492\u003c/li\u003e\n\u003cli\u003eForrest CB, Meltzer LJ, Marcus CL, de la Motte A, Kratchman A, Buysse DJ, et al. Development and validation of the PROMIS pediatric sleep disturbance and sleep-related impairment item banks. Sleep. 2018;41(6). doi:10.1093/sleep/zsy051\u003c/li\u003e\n\u003cli\u003eLandoll RR, La Greca AM, Lai BS, Chan SF, Herge WM. Cyber victimization by peers: prospective associations with adolescent social anxiety and depressive symptoms. J Adolesc. 2015;42:77\u0026ndash;86. doi:10.1016/j.adolescence.2015.04.002\u003c/li\u003e\n\u003cli\u003eMalecki CK, Demaray MK, Elliott SN, Nolten PW. Child and Adolescent Social Support Scale (CASSS). APA PsycTests. doi:10.1037/t57891-000\u003c/li\u003e\n\u003cli\u003eDemaray MK, Malecki CK. The relationship between perceived social support and maladjustment for students at risk. Psychol Sch. 2002;39(3):305\u0026ndash;316. doi:10.1002/pits.10018\u003c/li\u003e\n\u003cli\u003eHahn EA, DeWalt DA, Bode RK, Garcia SF, DeVellis RF, Correia H, et al. New English and Spanish social health measures will facilitate evaluating health determinants. Health Psychol. 2014;33(5):490\u0026ndash;499. doi:10.1037/hea0000038\u003c/li\u003e\n\u003cli\u003eMuth\u0026eacute;n LK, Muth\u0026eacute;n BO. Mplus (version 8.10) [computer software]. Los Angeles (CA): Muth\u0026eacute;n \u0026amp; Muth\u0026eacute;n; 2024.\u003c/li\u003e\n\u003cli\u003eR Core Team. R: a language and environment for statistical computing. Version 4.4.2. Vienna (Austria): R Foundation for Statistical Computing; 2024. https://www.R-project.org/.\u003c/li\u003e\n\u003cli\u003eYu DJ, Wing YK, Li TMH, Chan NY. The impact of social media use on sleep and mental health in youth: a scoping review. Curr Psychiatry Rep. 2024;26(3):104\u0026ndash;119. doi:10.1007/s11920-024-01481-9\u003c/li\u003e\n\u003cli\u003eAlonzo R, Hussain J, Stranges S, Anderson KK. Interplay between social media use, sleep quality, and mental health in youth: a systematic review. Sleep Med Rev. 2021;56:101414. doi:10.1016/j.smrv.2020.101414\u003c/li\u003e\n\u003cli\u003eVolpe VV, Benson GP, Czoty L, et al. Not just time on social media: experiences of online racial/ethnic discrimination and worse sleep quality for Black, Latinx, Asian, and multiracial young adults. J Racial Ethn Health Disparities. 2023;10:2312\u0026ndash;2319. doi:10.1007/s40615-022-01410-7\u003c/li\u003e\n\u003cli\u003eMcAlister KL, Beatty CC, Smith Caswell JE, Yourell JL, Huberty JL. Social media use in adolescents: bans, benefits, and emotion regulation behaviors. JMIR Ment Health. 2024;11:e64626. doi:10.2196/64626\u003c/li\u003e\n\u003cli\u003ePopat A, Tarrant C. Exploring adolescents\u0026rsquo; perspectives on social media and mental health and well-being: a qualitative literature review. Clin Child Psychol Psychiatry. 2022;28(1):323\u0026ndash;337. doi:10.1177/13591045221092884\u003c/li\u003e\n\u003cli\u003eKowalski RM, Giumetti GW, Schroeder AN, Lattanner MR. Bullying in the digital age: a critical review and meta-analysis of cyberbullying research among youth. Psychol Bull. 2014;140(4):1073\u0026ndash;1137. doi:10.1037/a0035618\u003c/li\u003e\n\u003cli\u003eWaasdorp TE, Bradshaw CP. The overlap between cyberbullying and traditional bullying. J Adolesc Health. 2015;56(5):483\u0026ndash;488. doi:10.1016/j.jadohealth.2014.12.002\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cybervictimization, Sleep, Emotional Support, Social Media","lastPublishedDoi":"10.21203/rs.3.rs-8502717/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8502717/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eReduced sleep quality is common in adolescents, and youth from marginalized backgrounds are at heightened risk for cybervictimization, a known contributor to sleep problems. Emotional support has been linked to better sleep but its role in youth experiencing cybervictimization is unclear. We examined whether in-person and SM emotional support moderate the association between cybervictimization and sleep quality in a national sample of youth\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eFrom November 2023 to March 2024, 689 youth of marginalized backgrounds (mean age 19, SD 1.52) recruited from social media completed a web-based survey assessing cybervictimization, sleep quality, in person peer emotional support, in person parent emotional support, and social media emotional support. Linear regression models tested moderation effects, adjusting for key demographic covariates.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eCybervictimization was associated with worse sleep quality (β\u0026thinsp;=\u0026thinsp;3.034, 95% CI [1.766, 4.302], p\u0026thinsp;\u0026lt;\u0026thinsp;.001). All forms of emotional support were associated with better sleep quality: In-person peer (β = \u0026minus;\u0026thinsp;0.134, 95% CI [\u0026ndash;0.195, \u0026minus;\u0026thinsp;0.072], p\u0026thinsp;\u0026lt;\u0026thinsp;.001), In-person parent (β = \u0026minus;\u0026thinsp;0.172, 95% CI [\u0026ndash;0.228, \u0026minus;\u0026thinsp;0.115], p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and SM-based (β = \u0026minus;\u0026thinsp;0.09, 95% CI [\u0026ndash;0.169, \u0026minus;\u0026thinsp;0.01], p\u0026thinsp;=\u0026thinsp;.0271). Only in-person peer emotional support (β\u0026thinsp;=\u0026thinsp;0.119, 95% CI [0.016, 0.222], p\u0026thinsp;=\u0026thinsp;.0236) significantly interacted with cybervictimization and sleep, showing a positive direction of effect.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eCybervictimization was associated with worse sleep, while both in-person and SM emotional support were associated with better sleep. We also found a complex interaction where teens who had increased in-person peer emotional support, moderated worse sleep quality associated with cybervictimization.\u003c/p\u003e","manuscriptTitle":"Association between Cybervictimization and Sleep Quality in a National Sample of U.S. Adolescents: Examining the Moderating Role of In-Person and Social Media Emotional Support","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-30 14:00:56","doi":"10.21203/rs.3.rs-8502717/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-09T11:26:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-08T14:40:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-02T09:06:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26911078624263173348624885910558698664","date":"2026-01-30T13:17:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177798387818538385133269758605160214280","date":"2026-01-29T08:12:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4585258266487280448853420357258283051","date":"2026-01-29T08:07:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300193696003079156521879204747033147647","date":"2026-01-28T11:54:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-28T08:32:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-19T07:30:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-07T05:13:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-07T03:24:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pediatrics","date":"2026-01-07T03:20:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e6804fee-fc15-416b-a51b-b29b654e84ae","owner":[],"postedDate":"January 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T16:06:11+00:00","versionOfRecord":{"articleIdentity":"rs-8502717","link":"https://doi.org/10.1186/s12887-026-06751-4","journal":{"identity":"bmc-pediatrics","isVorOnly":false,"title":"BMC Pediatrics"},"publishedOn":"2026-03-18 15:58:41","publishedOnDateReadable":"March 18th, 2026"},"versionCreatedAt":"2026-01-30 14:00:56","video":"","vorDoi":"10.1186/s12887-026-06751-4","vorDoiUrl":"https://doi.org/10.1186/s12887-026-06751-4","workflowStages":[]},"version":"v1","identity":"rs-8502717","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8502717","identity":"rs-8502717","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}